Papers BEV Segmentation
“BEV Segmentation” 태그가 달린 논문 61편 · 필터 해제
CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation
Collaborative perception breaks through single-view limitations via multi-agent information exchange. However, multi-source noise such as pose errors and communication delays degrades fusion feature quality, constraining…
BEV SegmentationVariational Inference for Bird's Eye View Segmentation in Autonomous Driving
The bird's eye view (BEV) has emerged as a pivotal approach for environmental perception in autonomous driving, providing a unified spatial representation for vehicles. Nevertheless, despite BEV's significance in address…
Autonomous DrivingBEV SegmentationOpen-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints
Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving. Despite recent progress, state-of-the-art methods remain confined to closed-set scenarios, making …
Autonomous DrivingBEV SegmentationGOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
Understanding road scenes in a geometrically consistent, scene-centric representation is crucial for planning and mapping. We present GOLD-BEV, a framework that learns dense bird's-eye-view (BEV) semantic environment map…
BEV SegmentationFishRoPE: Projective Rotary Position Embeddings for Omnidirectional Visual Perception
Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial representations assume the rectilinear geometry of pinhole cameras. Fishe…
Autonomous VehiclesBEV SegmentationHorizonWeaver: Generalizable Multi-Level Semantic Editing for Driving Scenes
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guided image editors, trained on object-centr…
Autonomous DrivingBEV SegmentationBEVMAPMATCH: Multimodal BEV Neural Map Matching for Robust Re-Localization of Autonomous Vehicles
Localization in GNSS-denied and GNSS-degraded environments is a challenge for the safe widespread deployment of autonomous vehicles. Such GNSS-challenged environments require alternative methods for robust localization. …
Autonomous VehiclesBEV SegmentationCycleBEV: Regularizing View Transformation Networks via View Cycle Consistency for Bird's-Eye-View Semantic Segmentation
Transforming image features from perspective view (PV) space to bird's-eye-view (BEV) space remains challenging in autonomous driving due to depth ambiguity and occlusion. Although several view transformation (VT) paradi…
Semantic SegmentationAutonomous DrivingBEV SegmentationFaster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation
Dense Bird's Eye View (BEV) semantic maps are central to autonomous driving, yet current multi-camera methods depend on costly, inconsistently annotated BEV ground truth. We address this limitation with a two-phase train…
Semantic SegmentationAutonomous DrivingBEV SegmentationGaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion
Robust and accurate perception of dynamic objects and map elements is crucial for autonomous vehicles performing safe navigation in complex traffic scenarios. While vision-only methods have become the de facto standard d…
Autonomous VehiclesBEV SegmentationKD360-VoxelBEV: LiDAR and 360-degree Camera Cross Modality Knowledge Distillation for Bird's-Eye-View Segmentation
We present the first cross-modality distillation framework specifically tailored for single-panoramic-camera Bird's-Eye-View (BEV) segmentation. Our approach leverages a novel LiDAR image representation fused from range,…
Knowledge DistillationAutonomous DrivingBEV SegmentationFisheyeGaussianLift: BEV Feature Lifting for Surround-View Fisheye Camera Perception
Accurate BEV semantic segmentation from fisheye imagery remains challenging due to extreme non-linear distortion, occlusion, and depth ambiguity inherent to wide-angle projections. We present a distortion-aware BEV segme…
Semantic SegmentationBEV SegmentationPandar128 dataset for lane line detection
We present Pandar128, the largest public dataset for lane line detection using a 128-beam LiDAR. It contains over 52,000 camera frames and 34,000 LiDAR scans, captured in diverse real-world conditions in Germany. The dat…
BEV SegmentationLine DetectionLane DetectionRate-Distortion Optimized Communication for Collaborative Perception
Collaborative perception emphasizes enhancing environmental understanding by enabling multiple agents to share visual information with limited bandwidth resources. While prior work has explored the empirical trade-off be…
3D Object DetectionBEV SegmentationCoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perception
Collaborative perception aims to extend sensing coverage and improve perception accuracy by sharing information among multiple agents. However, due to differences in viewpoints and spatial positions, agents often acquire…
3D Object DetectionBEV SegmentationFishBEV: Distortion-Resilient Bird's Eye View Segmentation with Surround-View Fisheye Cameras
As a cornerstone technique for autonomous driving, Bird's Eye View (BEV) segmentation has recently achieved remarkable progress with pinhole cameras. However, it is non-trivial to extend the existing methods to fisheye c…
Autonomous DrivingBEV SegmentationEnd-to-End Visual Autonomous Parking via Control-Aided Attention
Precise parking requires an end-to-end system where perception adaptively provides policy-relevant details - especially in critical areas where fine control decisions are essential. End-to-end learning offers a unified f…
BEV SegmentationNRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, as pivotal for real-world applications, u…
Autonomous DrivingBEV SegmentationSegmentationSemantic Segmentation+14D-ROLLS: 4D Radar Occupancy Learning via LiDAR Supervision
A comprehensive understanding of 3D scenes is essential for autonomous vehicles (AVs), and among various perception tasks, occupancy estimation plays a central role by providing a general representation of drivable and o…
Autonomous VehiclesBEV SegmentationGPURESAR-BEV: An Explainable Progressive Residual Autoregressive Approach for Camera-Radar Fusion in BEV Segmentation
Bird's-Eye-View (BEV) semantic segmentation provides comprehensive environmental perception for autonomous driving but suffers multi-modal misalignment and sensor noise. We propose RESAR-BEV, a progressive refinement fra…
Autonomous DrivingBEV SegmentationSemantic Segmentation